Search every byte

serverless vector and full-text search built from first principles on object storage: fast, 10x cheaper, and extremely scalable

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                        ╔═ turbopuffer ════════════════════════════╗
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║            ║░         ║  ┏━━━━━━━━━━━━━━━┓     ┏━━━━━━━━━━━━━━┓  ║░
║   client   ║░───API──▶║  ┃    Memory/    ┃────▶┃    Object    ┃  ║░
║            ║░         ║  ┃   SSD Cache   ┃     ┃ Storage (S3) ┃  ║░
╚════════════╝░         ║  ┗━━━━━━━━━━━━━━━┛     ┗━━━━━━━━━━━━━━┛  ║░
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         ╔════════════╗          
         ║            ║░         
      ┌──║   client   ║░         
      │  ║            ║░         
     API ╚════════════╝░         
      │   ░░░░░░░░░░░░░░         
      └─────────┐                
                │                
                ▼                
╔═ turbopuffer ════════════════╗ 
║                              ║░
║  ┏━━━━━━━━━━━━━━━━━━━━━━━━┓  ║░
║  ┃        Memory/         ┃  ║░
║  ┃       SSD Cache        ┃  ║░
║  ┗━━━━━━━━━━━━━━━━━━━━━━━━┛  ║░
║               │              ║░
║               ▼              ║░
║     ┏━━━━━━━━━━━━━━━━━━━┓    ║░
║     ┃Object storage (S3)┃    ║░
║     ┗━━━━━━━━━━━━━━━━━━━┛    ║░
║                              ║░
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150B+ documents & 6K+ queries/s in prod

Case study

Cost calculator

Workload768 dimensions, 1M docs, ~3GB

p50

16ms

402ms

p90

21ms

524ms

p99

33ms

677ms

Warm namespace

Cold namespace

Approach (3 QPS with topk=10)

turbopuffer/tpuf-benchmark

Aman Sanger

Co-founder

After switching our vector db to @turbopuffer, we're saving an order of magnitude in costs and dealing with far less complexity!

100B+

documents

10M+

namespaces

Case study

Justin Watts

Distinguished Engineer

Moving to turbopuffer felt less like an upgrade and more like discovering a new paradigm. We didn't just save costs; we turned once-prohibitive features into standard tools in our arsenal. Vectorize all the things and say goodbye to sharding

MTL 🇨🇦

single-tenant cluster

Jori Lallo

Co-founder

It's been fun to work with @turbopuffer on [the new search]. Admittedly keyword based search never really worked great for issues, and the results from embedding+FTS are now actually useful. We'll also use this new infrastructure to power more of Linear's AI functionality

Limits

Metric	Observed in production	Production limits (current)(upcoming)
Max documents (global)	150B+	Unlimited
Max documents (per namespace)	200M	100M1B+
Max number of namespaces	40M+	Unlimited
Max write rate (global)	1M docs/s	Unlimited
Max write rate (per namespace)	10K docs/s	10K docs/s
Max queries (global)	6K+ queries/s	Unlimited
Max queries (per namespace)	1K+ queries/s	1K+ queries/s10K queries/s
Vector search recall@10	90-100%	90-100%Configurable
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